Fluid dynamic assessment of tracheal flow in infants with congenital tracheal stenosis before and after surgery
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Tracheal flow in infants with congenital tracheal stenosis (CTS) was numerically investigated using subject-specific airway models before and after reconstructive surgery. We quantified tracheal flow based on airway resistance during inhalation, and compared it between controls and patients before and after surgery. The airway resistance in each subject was assessed using geometrical parameters of the trachea: the minimum cross-sectional area Amin, the minimum cross-sectional area normalized by the standard deviation of the cross-sectional area Amin/σA, the area ratio of the minimum and maximum cross-sectional area Amin/Amax, and ratio of the normalized standard deviation of cross-sectional area to the mean cross-sectional area σA/Amean. Our numerical results demonstrated that such geometrical parameters could be used to assess the severity of CTS. Since subjects can be more clearly categorized as controls and most preoperative patients in terms of the airway resistance, a simulation using subject-specific airway models can lead us to a precise understanding of tracheal flow, and also provide knowledge about therapeutic decision. Our numerical results also demonstrated that significant surgical expansion of cross-sectional area did not help recover tracheal flow because of expansion loss. These results will be helpful not only when making therapeutic decisions about surgery but also when assessing quality of life in postoperative patients.
KeywordsCongenital tracheal stenosis Tracheal flow Infants Fluid dynamics Computational biomechanics
This research was supported by JSPS KAKENHI Grant Number JP17K13015, and by the Keihanshin Consortium for Fostering the Next Generation of Global Leaders in Research (K-CONNEX), established by the Human Resource Development Program for Science and Technology, and also by Ministry of Education, Culture, Sports, Science, and Technology (MEXT) as “Priority Issue on post-K computer” (Integrated Computational Life Science to Support Personalized and Preventive Medicine) (Project ID: hp180202).
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Conflict of interest
The authors declare that they have no conflict of interest.
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